The development cycles for implementing and optimizing image processing algorithms for massively parallel processing on GPUs, on the other hand, are much shorter and thus less expensive. In addition, GPUs provide a much higher processing power, ideal for algorithms with high computational effort, such as stereo image processing. Early works [1]}, [2]} have utilized the rendering pipeline of OpenGL to deploy the *SGM stereo algorithm on graphics hardware and reached frame rates of up to 8 FPS and 4 FPS on VGA image resolution respectively. With the introduction of the CUDA-API in 2007, the development costs for *GPGPU have dropped even more. And so, a lot of implementations of the *SGM algorithm for real-time stereo processing on hardware without embedded constraints are optimized and deployed on graphics hardware [3]}, [4]}, [5]}. [5]} show that, with increasing computational power, the use of *GPGPU on modern, high-performing graphics hardware, such as the NVIDIA Titan X, allows to reach frame rates of up to 237 FPS on VGA image resolution with the conventional use of eight optimization paths inside the *SGM optimization. Even higher frame rates of up to 475 FPS and 886 FPS are possible, if the number of optimization paths are reduced to four or two respectively.
